TY - JOUR
T1 - Textual inference for eligibility criteria resolution in clinical trials
AU - Shivade, Chaitanya
AU - Hebert, Courtney
AU - Lopetegui, Marcelo
AU - de Marneffe, Marie Catherine
AU - Fosler-Lussier, Eric
AU - Lai, Albert M.
N1 - Funding Information:
We would like to thank our annotators Alissa Schultz, Jennifer Fox and Jessica Schellenbach for their help in the annotation effort. Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under award number R01LM011116 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed toward specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-score = 26.25), but it is a promising direction for further research.
AB - Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed toward specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-score = 26.25), but it is a promising direction for further research.
KW - Clinical trials
KW - Electronic health records
KW - Natural language processing
KW - Textual inference
UR - http://www.scopus.com/inward/record.url?scp=84946238936&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2015.09.008
DO - 10.1016/j.jbi.2015.09.008
M3 - Article
C2 - 26376462
AN - SCOPUS:84946238936
SN - 1532-0464
VL - 58
SP - S211-S218
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
ER -